BoneMRI

K233030

MRIguidance B.V · cleared 2024-03-01 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
The BoneMRI application is a standalone image processing software application that analyses 3D gradient echo MRI scans acquired with a dedicated MRI scan protocol.
Algorithman image enhancement algorithm using convolutional neural network. Original images are enhanced by running them through a cascade of filter banks, where thresholding and scaling operations are applied. Separate neural network-based filters are obtained to assign a Hounsfield Unit (HU) value to a single volume element, based on intensity and contextual information. The parameters of the model were obtained through an algorithm development pipeline.
source quote (p.8)
MRIguidance software implements an image enhancement algorithm using convolutional neural network. Original images are enhanced by running them through a cascade of filter banks, where thresholding and scaling operations are applied. Separate neural network-based filters are obtained to assign a Hounsfield Unit (HU) value to a single volume element, based on intensity and contextual information. The parameters of the model were obtained through an algorithm development pipeline.
Adaptive (vs locked)No
source quote (p.9)
In accordance with the PCCP, all algorithm modifications will be trained, tuned, and locked prior to release of the application.
PCCPYes
source quote (p.9)
Addition of a Predetermined Change Control Plan to support an iterative development approach for the machine learning models in the BoneMRI application.
Cybersecurity addressedYes
source quote (p.6)
The BoneMRI application is a server application running on the clinic or hospital networks. It is available as fully on-premise software with specific GPU hardware requirements, or partly running as a managed cloud service, for which the environment in which the managed modules run is controlled by MRIguidance. The on-premise software is fully controlled by the clinic or hospital, and as such, no protected health information (PHI) will leave the clinic or hospital network. All data sent to the managed cloud server will be de-identified before it leaves the clinic or hospital network, and as such, the managed cloud service will not receive PHI.

Validation studies (1)

Retrospective clinical

n=193 patients

endpoints: Cortical delineation error (mm); Mean deviation in all tissue and bone (HU); Correlation coefficient in bone

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
3
MAUDE reports in code, 12mo
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K233030